Vestibular contribution to path integration deficits in 'at-genetic-risk' for Alzheimer's disease.

Path integration changes may precede a clinical presentation of Alzheimer's disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer's disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer's disease risk carriers.

Using GPS Tracking to Investigate Outdoor Navigation Patterns in Patients With Alzheimer Disease: Cross-sectional Study.

BACKGROUND: Spatial disorientation is one of the earliest and most distressing symptoms seen in patients with Alzheimer disease (AD) and can lead to them getting lost in the community. Although it is a prevalent problem worldwide and is associated with various negative consequences, very little is known about the extent to which outdoor navigation patterns of patients with AD explain why spatial disorientation occurs for them even in familiar surroundings. OBJECTIVE: This study aims to understand the outdoor navigation patterns of patients with AD in different conditions (alone vs accompanied; disoriented vs not disoriented during the study) and investigate whether patients with AD experienced spatial disorientation when navigating through environments with a high outdoor landmark density and complex road network structure (road intersection density, intersection complexity, and orientation entropy). METHODS: We investigated the outdoor navigation patterns of community-dwelling patients with AD (n=15) and age-matched healthy controls (n=18) over a 2-week period using GPS tracking and trajectory mining analytical techniques. Here, for the patients, the occurrence of any spatial disorientation behavior during this tracking period was recorded. We also used a spatial buffer methodology to capture the outdoor landmark density and features of the road network in the environments that the participants visited during the tracking period. RESULTS: The patients with AD had outdoor navigation patterns similar to those of the controls when they were accompanied; however, when they were alone, they had significantly fewer outings per day (total outings: P<.001; day outings: P=.003; night outings: P<.001), lower time spent moving per outing (P=.001), lower total distance covered per outing (P=.009), lower walking distance per outing (P=.02), and lower mean distance from home per outing (P=.004). Our results did not identify any mobility risk factors for spatial disorientation. We also found that the environments visited by patients who experienced disorientation versus those who maintained their orientation during the tracking period did not significantly differ in outdoor landmark density (P=.60) or road network structure (road intersection density: P=.43; intersection complexity: P=.45; orientation entropy: P=.89). CONCLUSIONS: Our findings suggest that when alone, patients with AD restrict the spatial and temporal extent of their outdoor navigation in the community to successfully reduce their perceived risk of spatial disorientation. Implications of this work highlight the importance for future research to identify which of these individuals may be at an actual high risk for spatial disorientation as well as to explore the implementation of health care measures to help maintain a balance between patients' right to safety and autonomy when making outings alone in the community.

Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies.

Unlike most other health conditions, the treatment of mental illness relies on subjective measurement. In addition, the criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behavior in situ. This promises to lead to more precise assessment of human behavior and ultimately individual mental health. More refined modeling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare. In this paper, we provide a conceptual review of such techniques for measuring, modeling, and treating mental illness and maintaining mental health.